The ATF/CREB Family of Transcription Factors in Breast Cancer

Haakenson, Jeremy K.; Kester, Mark; Zlobec, Inti

doi:10.5772/21349

Cited by 1 publication

(1 citation statement)

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“…A nucleotide T deletion in the activating transcription factor 1 ( ATF1 ) gene causes a splice region variant, which was observed in all samples reported by id rs4986837. The role of ATF1 in breast cancer has been identified as a tumor suppressor ( Huang et al, 2016 ), including its activation by BRCA1, increasing transcription of MHC class genes and its involvement in regulating the steroidal hormone synthesis ( Haakenson, Kester & Liu, 2012 ). A splice region variant could be responsible for the loss of an exon or the inclusion of an intron resulting in an altered protein sequence.…”

Section: Resultsmentioning

confidence: 99%

Integration of exome-seq and mRNA-seq using DawnRank, identified genes involved in innate immunity as drivers of breast cancer in the Indian cohort

Nirgude,

Desai,

Khanchandani

et al. 2023

PeerJ

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Genetic heterogeneity influences the prognosis and therapy of breast cancer. The cause of disease progression varies and can be addressed individually. To identify the mutations and their impact on disease progression at an individual level, we sequenced exome and transcriptome from matched normal-tumor samples. We utilised DawnRank to prioritise driver genes and identify specific mutations in Indian patients. Mutations in the C3 and HLA genes were identified as drivers of disease progression, indicating the involvement of the innate immune system. We performed immune profiling on 16 matched normal/tumor samples using CIBERSORTx. We identified CD8+ve T cells, M2 macrophages, and neutrophils to be enriched in luminal A and T cells CD4+naïve, natural killer (NK) cells activated, T follicular helper (Tfh) cells, dendritic cells activated, and neutrophils in triple-negative breast cancer (TNBC) subtypes. Weighted gene co-expression network analysis (WGCNA) revealed activation of T cell-mediated response in ER positive samples and Interleukin and Interferons in ER negative samples. WGCNA analysis also identified unique pathways for each individual, suggesting that rare mutations/expression signatures can be used to design personalised treatment.

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Section: Resultsmentioning

confidence: 99%